Cough Monitoring Through Audio Analysis

The detection of cough events in audio recordings requires the analysis of a significant amount of data as cough is typically monitored continuously over several hours to capture naturally occurring cough events. The recorded data is mostly composed of undesired sound events such as silence, background noise, and speech. To reduce computational costs and to address the ethical concerns raised from the collection of audio data in public environments, the data requires pre-processing prior to any further analysis. Current cough detection algorithms typically use pre-processing methods to remove undesired audio segments from the collected data but do not preserve the privacy of individuals being recorded while monitoring respiratory events. This study reveals the need for an automatic pre-processing method that removes sensitive data from the recording prior to any further analysis to ensure privacy preservation of individuals. Specific characteristics of cough sounds can be used to discard sensitive data from audio recordings at a pre-processing stage, improving privacy preservation, and decreasing ethical concerns when dealing with cough monitoring through audio analysis. We propose a pre-processing algorithm that increases privacy preservation and significantly decreases the amount of data to be analysed, by separating cough segments from other non-cough segments, including speech, in audio recordings. Our method verifies the presence of signal energy in both lower and higher frequency regions and discards segments whose energy concentrates only on one of them. The method is iteratively applied on the same data to increase the percentage of data reduction and privacy preservation. We evaluated the performance of our algorithm using several hours of audio recordings with manually pre-annotated cough and speech events. Our results showed that 5 iterations of the proposed method can discard up to 88.94% of the speech content present in the recordings, allowing for a strong privacy preservation while considerably reducing the amount of data to be further analysed by 91.79%. The data reduction and privacy preservation achievements of the proposed pre-processing algorithm offers the possibility to use larger datasets captured in public environments and would beneficiate all cough detection algorithms by preserving the privacy of subjects and by-stander conversations recorded during cough monitoring

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Análisis de señales de tos para detección temprana de enfermedades respiratorias

Antecedentes: La tos es un movimiento sonoro y convulsivo del aparato respiratorio. Hasta ahora, el análisis de la tos como síntoma informativo de la evolución de una enfermedad se limita a herramientas de medición subjetivas, o incómodos monitores de la tos. Otro limitante actual, se debe a que los métodos de procesamiento de audio implementados en dichos monitores no pueden hacer frente a entornos ruidosos, como en el caso en que el dispositivo de adquisición sea un smartphone que el paciente pueda llevar en su bolsillo. Objetivo: El objetivo de este Trabajo de Fin de Grado (TFG) es diseñar diseñar un sistema de “audición máquina” (Machine Hearing) mediante una arquitectura de aprendizaje profundo (Deep Learning) para realizar la detección de tos, así como la detección de enfermedades respiratorias con carácter temprano a partir de señales de audio ruidosas. Métodos: Para realizar el proyecto, se han utilizado señales de audio ruidosas de veinte pacientes con diferentes enfermedades respiratorias, 18433 señales de audio grabadas durante episodios de tos y 18433 señales de audio grabadas durante episodios sin tos. Dichas señales de audio son preprocesadas en tres pasos. Primero, se segmentan las señales de audio originales (señales de tos y no tos) para que todas tengan una duración de un segundo. En segundo lugar, se realiza un espectrograma logarítmico a cada audio para transformar las señales 1D temporales en imágenes (señales 2D) tiempo-frecuencia. Finalmente, se normalizan los datos para poder alimentar a una red neuronal convolucional (Convolutional Neural Network, CNN), que realiza automáticamente la extracción de características en los espectrogramas de los audios para identificar “firmas” espectrales o temporales. De esta forma en primer lugar se detecta si dicho audio contiene una tos o no, y en caso de que la contenga, se pasaría al diagnóstico de la enfermedad respiratoria. Resultados: El sistema de detección de audios con toses tiene una sensibilidad del 85,64% y una especificidad del 92,81 %. Con respecto a la detección temprana de enfermedades respiratorias, se ha alcanzado una tasa de acierto del 77,78% cuando el sistema diagnostica si un paciente tiene tos aguda o enfermedad pulmonar obstructiva crónica (Chronic Obstructive Pulmonary Disease, COPD), superando a los métodos más modernos. Conclusiones: Los resultados de este TFG allanan el camino para crear un dispositivo cómodo y no intrusivo, con una interrupción mínima en las actividades diarias, que pueda detectar con carácter temprano enfermedades respiratorias, beneficiando a pacientes, profesionales sanitarios y sistemas nacionales de salud.Grado en Ingeniería de Tecnologías de Telecomunicació